Use of an intrinsic image in face recognition

ABSTRACT

In an exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, identifying information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color; defining a constraint as a function of the information; and utilizing the constraint in an image segregation operation.

BACKGROUND OF THE INVENTION

In recent years, considerable progress has been made in the development of computer programs that perform an analysis of digital images of human faces in order to identify the individual or individuals depicted in the image. However, varying conditions that may exist at the time the digital image was taken can interfere with an accurate performance of a face recognition task. For example, varying illumination conditions existing at the time an image is recorded can result in shadows cast across a face depicted in the image. Such shadows can prevent a computer system from performing an accurate analysis of facial features depicted in the image, and thus, cause a failure to properly identify the individual. Accordingly, there is a persistent need for the development of accurate and efficient techniques that can be utilized in the operation of computers relating to images, to provide improved performance in, for example, a face recognition method.

SUMMARY OF THE INVENTION

The present invention provides a method and system implementing image processing techniques that analyze aspects of an image, for example, information in the image relevant to a logical deduction regarding material and illumination aspects of an image, and including basic components of color such as intensity and chromaticity. In an exemplary embodiment of the present invention, tone mapping and spatio-spectral information relevant to an image are utilized to perform an image segregation operation, to thereby generate intrinsic images corresponding to the original image. The intrinsic images can include, for example, a material image, to capture reflectance properties of surfaces depicted in an image (the percentage of each wavelength of light a surface reflects) and an illumination image, to capture the intensity and color of light incident upon each point on the surfaces depicted in the image. The generated intrinsic images are generated in a manner suitable to achieve higher performance and more accurate results in a task such as, for example, face recognition analysis of a digital image.

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, identifying information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, defining a constraint as a function of the information and utilizing the constraint in an image segregation operation.

In a second exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, organizing information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image and utilizing the matrix equation in an image segregation operation.

In a third exemplary embodiment of the present invention, a computer system is provided. The computer system comprises a CPU and a memory storing an image file containing an image. According to a feature of the present invention, the CPU is arranged and configured to execute a routine to identify information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, define a constraint as a function of the information and utilize the constraint in an image segregation operation.

In a fourth exemplary embodiment of the present invention, a computer system is provided. The computer system comprises a CPU and a memory storing an image file containing an image. According to a feature of the present invention, the CPU is arranged and configured to execute a routine to organize information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image and utilize the matrix equation in an image segregation operation.

In a fifth exemplary embodiment of the present invention, a computer program product is provided. According to a feature of the present invention, the computer program product is disposed on a computer readable media, and the product includes computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory, identify information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, define a constraint as a function of the information and utilize the constraint in an image segregation operation.

In a sixth exemplary embodiment of the present invention, a computer program product is provided. According to a feature of the present invention, the computer program product is disposed on a computer readable media, and the product includes computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory, organize information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image; and utilize the matrix equation in an image segregation operation.

In accordance with yet further embodiments of the present invention, computer systems are provided, which include one or more computers configured (e.g., programmed) to perform the methods described above. In accordance with other embodiments of the present invention, computer readable media are provided which have stored thereon computer executable process steps operable to control a computer(s) to implement the embodiments described above. The present invention contemplates a computer readable media as any product that embodies information usable in a computer to execute the methods of the present invention, including instructions implemented as a hardware circuit, for example, as in an integrated circuit chip. The automated, computerized methods can be performed by a digital computer, analog computer, optical sensor, state machine, sequencer, integrated chip or any device or apparatus that can be designed or programed to carry out the steps of the methods of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer system arranged and configured to perform operations related to images.

FIG. 2 shows an n×m pixel array image file for an image stored in the computer system of FIG. 1.

FIG. 3 is a flow chart for performance of an image segregation operation implemented for improved performance of a task such as a face recognition analysis.

FIG. 4 is a functional block diagram of an image segregation system architecture, implemented in the computer system of FIG. 1, according to a feature of the present invention.

FIG. 5 shows a graphical user interface for use in connection with an implementation of the image segregation system architecture feature of the present invention.

FIG. 6 a is a flow chart for identifying Type C token regions in the image file of FIG. 2 a, according to a feature of the present invention.

FIG. 6 b is an original image used as an example in the identification of Type C tokens.

FIG. 6 c shows Type C token regions in the image of FIG. 6 b.

FIG. 6 d shows Type B tokens, generated from the Type C tokens of FIG. 6 c, according to a feature of the present invention.

FIG. 7 is a flow chart for a routine to test Type C tokens identified by the routine of the flow chart of FIG. 6 a, according to a feature of the present invention.

FIG. 8 is a flow chart for constructing Type B tokens via an arbitrary boundary removal technique, according to a feature of the present invention.

FIG. 9 is a flow chart for creating a token graph, containing token map information, according to a feature of the present invention.

FIG. 10 is a flow chart for constructing Type B tokens via an adjacent planar token merging technique, according to a feature of the present invention.

FIG. 11 is a flow chart for generating Type C tokens via a local token analysis technique, according to a feature of the present invention.

FIG. 12 is a flow chart for constructing Type B tokens from Type C tokens generated via the local token analysis technique of FIG. 11, according to a feature of the present invention.

FIG. 13 is a representation of an [A] [x]=[b] matrix relationship according to a feature of the present invention.

FIG. 14 is a functional block diagram for a service provider component for use in the image segregation system architecture of FIG. 4.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawings, and initially to FIG. 1, there is shown a block diagram of a computer system 10 arranged and configured to perform operations related to images. A CPU 12 is coupled to a device such as, for example, a digital camera 14 via, for example, a USB port. The digital camera 14 operates to download images stored locally on the camera 14, including images depicting human faces, to the CPU 12. The CPU 12 stores the downloaded images in a memory 16 as image files 18. The image files 18 can be accessed by the CPU 12 for display on a monitor 20, or for print out on a printer 22.

Alternatively, the CPU 12 can be implemented as a microprocessor embedded in a device such as, for example, the digital camera 14 or a robot. The CPU 12 can also be equipped with a real time operating system for real time operations related to images, in connection with, for example, a robotic operation or an interactive operation with a user.

As shown in FIG. 2, each image file 18 comprises an n×m pixel array. Each pixel, p, is a picture element corresponding to a discrete portion of the overall image. All of the pixels together define the image represented by the image file 18. Each pixel comprises a digital value corresponding to a set of color bands, for example, red, green and blue color components (RGB) of the picture element. The present invention is applicable to any multi-band image, where each band corresponds to a piece of the electro-magnetic spectrum. The pixel array includes n rows of m columns each, starting with the pixel p (1,1) and ending with the pixel p(n, m). When displaying or printing an image, the CPU 12 retrieves the corresponding image file 18 from the memory 16, and operates the monitor 20 or printer 22, as the case may be, as a function of the digital values of the pixels in the image file 18, as is generally known.

According to a feature of the present invention, in an image process, the CPU 12 operates to analyze and process information, for example, the RGB values of the pixels of an image stored in an image file 18, to achieve various objectives, such as, for example, an analysis of fundamental aspects of an image, including basic components of color, such as intensity and chromaticity, utilizing tone mapping and spatio-spectral information relevant to an image, in the performance an image segregation operation. The image segregation provides intrinsic images corresponding to the original image, such as, for example, a material image and an illumination image. Pursuant to an exemplary embodiment of the present invention, the intrinsic images are generated to provide depictions of, for example, a face, in a manner to retain local shading contributing to the curvature appearance of significant features of a face, including the nose, chin, and so on, while removing strong shadowing that could interfere with an analysis of facial features, for more efficient and accurate face recognition.

To that end, FIG. 3 shows a high level flow chart depicting an exemplary embodiment of an image segregation operation implemented to achieve an improved performance of a task such as a face recognition analysis. According to a feature of the present invention, the image segregation operation performed by the CPU 12 implements a process for determination of constraints based upon basic components of color, for example, intensity and chromaticity aspects of an image, which are utilized in a constraint/solver model operable to identify the illumination and material components of the image.

Referring to FIG. 3, in step 1000, an input image file 18 is provided for analysis. Pursuant to the constraint/solver model feature of the present invention, steps 1002 and 1004 relate to constraint generation. In step 1002, constraints relative to intensity aspects of the input image file 18 are generated, for example, based upon a tone mapping of the image file 18, and in step 1004, constraints relative to chromaticity aspects of the input image file 18 are generated, for example, based upon spatio-spectral operators identified in the input image file 18. The constraints of steps 1002 and 1004 can be generated by the CPU 12 serially or in parallel. The constraints of steps 1002 and 1004, once generated, are concatenated in a matrix equation, as will be described in detail below, and input to a solver step 1006. The solver step 1006 performs a mathematical process that operates to obtain an optimized solution for the matrix equation, for generation of an intrinsic image, such as a material image and/or an illumination image derived from the original image stored in the input image file 18, as a function of the constraints generated in steps 1002 and 1004, as will appear.

In step 1008, the intrinsic image is output, and in step 1010, the generated intrinsic image, for example, the material image, is input to a face recognition step. According to a feature of the present invention, for improved results, the face recognition step can be implemented using a database of known faces represented by, for example, intrinsic material images generated from digital images of the known faces, according to the teachings of the present invention. The intrinsic image input to the face recognition step can be matched to the database of known faces, using any known face recognition algorithm.

As noted above, the constraints based upon the intensity aspects of the image are generated by the CPU 12 based upon a tone mapping of the image. Tone mapping is a technique for preserving local variation within the image, while reducing global variation. Specific known methods for performing tone mapping on an image are described in “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” Frédo Durand and Julie Dorsey, ACM Transactions of Graphics (Proceedings of the ACM SIGGRAPH '02 Conference), and “A Fast Approximation of the Bilateral Filter using a Signal Processing Approach,” Sylvain Paris and Frédo Durand, European Conference on Computer Vision (ECCV '06).

Performance of the technique separates an image into a “level” channel and a “detail” channel. The level channel depicts large scale variations of the image, without details, which are depicted in the detail channel. As such, the level channel is a reasonable approximation of illumination aspects of the image, and the detail channel is a reasonable approximation of material. In the context of a face recognition task, the detail channel provides an intensity specification of each pixel of the image, in a manner that retains the shading (the appearance of curvature), while significantly reducing the influence of shadowing (such as on a face illuminated from one side, or a shadow cast by the chin or a hat, and so on). Accordingly, the detail channel is utilized in a constraint relative to intensity aspects of the input image 18, as will be described below.

Spatio-spectral information is derived from a fundamental observation underlying a basic discovery of the present invention, is that an image comprises two components, material and illumination. All changes in an image are caused by one or the other of these components. Spatio-spectral information is information relevant to contiguous pixels of an image depicted in an image file 18, such as spectral relationships among contiguous pixels, in terms of color bands, for example RGB values of the pixels, and the spatial extent of the pixel spectral characteristics relevant to a characteristic of the image, such as, for example, a single material depicted in the image or illumination flux effecting the image. When one of material and illumination is known in an image, the other can be readily deduced.

Moreover, the illumination flux includes an incident illuminant and an ambient illuminant. The spectra for the incident illuminant and the ambient illuminant can be different from one another. Thus, a spectral shift is caused by a shadow, i.e., a decrease of the intensity of the incident illuminant. The spectral shift can cause a variance in color of material depicted in the scene, that is, a variance in measured color due to the spectral shift from full shadow, through the shadow penumbra, to fully lit. Pursuant to a feature of the present invention, spectral shift phenomena is captured in spatio-spectral information. The spatio-spectral information includes a spectral ratio: a ratio based upon a difference in color or intensities between two areas of a scene depicted in an image, which may be caused by different materials (an object edge), an illumination change (illumination boundary) or both.

According to the constraint/solver model feature of the present invention, spatio-spectral operators are generated to facilitate the process for the segregation of illumination and material aspects of a scene depicted in the retrieved image file 18. Spatio-spectral operators comprise representations or characteristics of an image that encompass spatio-spectral information usable in the processing of material and illumination aspects of an image. The spatio-spectral operators are subject to constraints that define constraining spatio-spectral relationships between the operators, for input to a solver. The solver includes a mathematical processing engine that operates to obtain an optimized solution for the generation of an intrinsic image, such as a material image and/or an illumination image derived from the original image stored in the retrieved image file 18, as a function of the constraining relationships between the spatio-spectral operators.

Spatio-spectral operators include, for example, tokens, token map information, log chromaticity representation values, X-junctions, BIDR model representations, a boundary representation, and a texton histogram based pixel representation.

Pursuant to a feature of the present invention, a token is a connected region of an image wherein the pixels of the region are related to one another in a manner relevant to identification of image features and characteristics such as identification of materials and illumination. The use of tokens recognizes the fact that a particular set of material/illumination/geometric characteristics of an image extends beyond a single pixel, and therefore, while the image processing described herein can be done on a pixel level, tokens expedite a more efficient processing of image properties. The pixels of a token can be related in terms of either homogeneous factors, such as, for example, close correlation of color values among the pixels, or nonhomogeneous factors, such as, for example, differing color values related geometrically in a color space such as RGB space, commonly referred to as a texture. Exemplary embodiments of the present invention provide methods and systems to identify various types of homogeneous or nonhomogeneous tokens for improved processing of image files. The present invention utilizes spatio-spectral information relevant to contiguous pixels of an image depicted in an image file 18 to identify token regions.

According to one exemplary embodiment of the present invention, homogeneous tokens are each classified as either a Type A token, a Type B token or a Type C token. A Type A token is a connected image region comprising contiguous pixels that represent the largest possible region of the image encompassing a single material in the scene. A Type B token is a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material in the scene, though not necessarily the maximal region corresponding to that material. A Type C token comprises a connected image region of similar image properties among the contiguous pixels of the token, for example, similar color and intensity, where similarity is defined with respect to a noise model for the imaging system used to record the image.

Type C tokens are also used in connection with the tone mapping technique, to represent a value V, relative to intensity aspects depicted in the detail channel of an input image file 18. For each of the contiguous pixels, p, of a Type C token, a calculation is made for log p_R+log p_G+log p_B, as an expression of the log material intensity for the RGB values of a particular pixel p, as provided by the detail channel result for the pixel. To that end, in the log domain, image=light+material. The image is the log of the original image intensity, and light is the log local average intensity estimated in the level channel. The detail channel provides the log material (material=image−light). V is an average for the calculated intensity values for the pixels, across all of the pixels of the respective Type C token, and is used in a constraint, as will be described below.

A linear token is a nonhomogeneous token comprising a connected region of the image wherein adjacent pixels of the region have differing color measurement values that fall within a cylinder in RGB space, from a dark end (in shadow) to a bright end (lit end), along a positive slope. The cylinder configuration is predicted by a bi-illuminant dichromatic reflection model (BIDR model), according to a feature of the present invention, when the color change is due to an illumination change forming a shadow (i.e. a decrease in the intensity of the incident illuminant as the interplay between the incident or direct illuminant and the ambient illuminant in the illumination field) over a single material of a scene depicted in the image.

For purposes of describing, identifying and using linear tokens, the BIDR model can be stated as: I_((x, y, z, θ, φ, λ))=c_(b)(λ)l_(d)(λ)γ_(b)+M_(a)(λ)c_(b)(λ), where: I_((x, y, z, θ, φ, λ)) is the radiance of a surface point at (x, y, z) in the direction θ, φ for the wavelength λ, c_(b) (λ) is the geometry independent body reflectance of a surface for the wavelength λ, l_(d) (λ) is the incident illuminant for the wavelength λ. γ_(b) is the product of a shadow factor s_(x, y, z) and a geometric factor m_(b) (θ_(i)), and M_(a) (λ) is the integral of the ambient illuminant and geometric body reflectance over a hemisphere, excluding the incident illuminant. For more detailed information on the BIDR model, reference should be made to U.S. application Ser. No. 11/341,751, filed Jan. 27, 2006, entitled: “Bi-illuminant Dichromatic Reflection Model For Image Manipulation,” published as US 2007/0176940 on Aug. 2, 2007.

Token map information indicates locations of tokens within an image, relative to one another. The map information is used to identify neighboring tokens for performing an analysis of token neighbor relationships relevant to constraining spatio-spectral relationships between tokens, for input to the solver.

Log chromaticity representation values provide illumination invariant values for pixels of the image. Logarithmic values of the color band values of the image pixels are plotted on a log-color space graph. The logarithmic values are then projected to a log-chromaticity projection plane oriented as a function of the BIDR model. The chromaticity plane values are substituted for the color band values (for example, RGB values) of each pixel. For more detailed information on log chromaticity representation values, reference should be made to U.S. application Ser. No. 11/403,719, filed Apr. 13, 2006, entitled: “Method And System For Separating Illumination And Reflectance Using a Log Color Space,” published as US 2007/0242878 on Oct. 18, 2007.

An X-junction is an area of an image where a material edge and an illumination boundary cross one another. An X-junction is an optimal location for an accurate analysis of material and illumination aspects of an image.

A boundary representation is an arrangement of pixels, on each side of a boundary, formed by, for example, adjacent Type B tokens. The arrangement is used to facilitate an analysis of the boundary to classify the boundary as a material boundary on a smooth surface (as opposed to another type of boundary, for example, an illumination edge, depth boundary or simultaneous illumination and material change). The pixel representation is configured to provide samples of pixels within each of the Type B tokens forming the boundary. The pixels of the samples are subject to spatio-spectral analysis, and the results are compared to determine the likelihood that the respective boundary corresponds to a material change.

A texton is a homogeneous representation for a region of an image that comprises a texture. Image texture can be defined as a function of spatial variation in pixel intensities. Image texture patterns are frequently the result of physical or reflective properties of the image surface. Commonly, an image texture is associated with spatial homogeneity and typically includes repeated structures, often with some random variation (e.g., random positions, orientations or colors). Image textures are also often characterized by certain visual properties such as regularity, coarseness, contrast and directionality. An example of image texture is the image of a zebra skin surface as it appears to be spatially homogenous and seems to contain variations of color intensities which form certain repeated patterns. Some image textures can be defined by geometric characteristics, such as stripes or spots. A texton based operator transforms patterns of differing reflectance caused by a textured material into a homogeneous representation that captures the spectral and spatial characteristics of the textured region in the image.

Constraints comprise, relative to the spatio-spectral operators described above, for example, an anchor constraint, a same illumination constraint, a smooth illumination constraint, a Type B token constraint, a Linear token constraint, a BIDR enforcement constraint, a same texton histogram constraint, a log chromaticity similarity constraint, an X junction constraint, and a boundary representation constraint. Each constraint is configured as a constraint generator software module that defines the spatio-spectral operators utilized by the respective constraint and provides an expression of the constraining relationship imposed upon the constituent operators.

An anchor constraint utilizes a number of brightest/largest Type C tokens in an image. The constraining relationship is that the material of the selected brightest/largest Type C tokens is constrained to be an absolute value for the color/brightness observed in the image. The constraint anchors a material map for the image at an absolute brightness to avoid relative brightness constraints.

A same illumination constraint utilizes Type C tokens and Type B tokens identified in an image and token map information. The constraining relationship is that adjacent Type C tokens, as indicted by the token map information, are at the same illumination, unless the adjacent Type C tokens are part of the same Type B token. The term “same” in connection with the term “illumination” is used to mean an average value with respect to a noise model for the imaging system used to record the image. This constrains any observed differences in appearance between adjacent Type C tokens, that are not part of the same Type B token, to be a material change, as will appear.

A smooth illumination constraint is similar to the same illumination constraint. However, rather than constraining all pixels of adjacent Type C tokens to be of the same illumination, as in the same illumination constraint, in the smooth illumination constraint, the constraint is based upon the average illumination of the pixels near a shared boundary between adjacent Type C tokens. This constrains the illumination field to be somewhat smooth, as opposed to piecewise constant (the same, as defined above) throughout a token.

A Type B token constraint also utilizes Type C tokens and Type B tokens. However, the constraining relationship is that all Type C tokens that are part of the same Type B token are constrained to be of the same material. This constraint enforces the definition of a Type B token, that is, a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material in the scene, though not necessarily the maximal region corresponding to that material. Thus, all Type C tokens that lie within the same Type B token are by the definition imposed upon Type B tokens, of the same material, though not necessarily of the same illumination. The Type C tokens are therefore constrained to correspond to observed differences in appearance that are caused by varying illumination.

Accordingly, the Type B token constraint is complementary to the same and smooth illumination constraints, which, as opposed to illumination change, constrain observed differences to correspond to material change, as described above. This is due to the fact that in each of the same and smooth illumination constraints, Type C tokens that are adjacent and not part of the same Type B token, are constrained to the same illumination. These Type C tokens should comprise different materials, since by the constraint, they are not in the same Type B token and therefore, by the definition of Type B tokens enforced by the constraint, do not encompass a single material, so illumination should be a constant, and any observed difference is considered as attributable to a material change.

To summarize, pursuant to a feature of the present invention, the Type C and Type B token spatio-spectral operators are defined to provide characteristics of an image that enable segregation of illumination and material. Type C tokens each comprise a connected image region of similar image properties, for example similar color, as recorded and stored in an image file 18. Thus, adjacent Type C tokens indicate some form of change in the image or else they would form the same Type C token. Type B tokens encompass a single material. The complementary constraints of the same/smooth illumination constraints and the Type B token constraint enforce relationships between the tokens that indicate either a material change or an illumination change.

If the adjacent Type C tokens are within the same type B token, as in the Type B token constraint, the differences between them should correspond to illumination change due to the same material property of the common Type B token. If the adjacent Type C tokens are not within the same Type B token, as in the same/smooth illumination constraints, the difference between them should then correspond to a material change since they are not both defined by a common, single material Type B token.

A Linear token constraint utilizes Type C tokens and Linear tokens. The constraining relationship is that a difference between two Type C tokens, spaced by a Linear token, approximately equals a characteristic illuminant spectral ratio for the image. As defined, a Linear token follows a cylinder configuration along a positive slope, through color space. The BIDR model predicts that the positive slope equals a characteristic illuminant spectral ratio for the image. Thus, the color difference between two Type C tokens, one at each of the dark end and bright end of a Linear token, should reflect the value of the respective characteristic illuminant spectral ratio for the image.

A BIDR enforcement constraint utilizes Type C tokens and a BIDR model defined normal vector for the log-chromaticity projection plane. The constraining relationship is that the illumination for all Type C tokens in a local patch of the image forms a set of parallel lines in log-color space, the orientation of the parallel lines being defined by the BIDR model defined normal vector. The constraint therefore enforces the illumination field present in the image to explicitly fit the BIDR model prediction for the illumination.

Thus, each of the Linear token constraint and the BIDR enforcement constraint utilize BIDR model predictions as a basis to segregate illumination and material aspects of an image. The BIDR model predicts a color change in an image when the color change is due to an illumination change forming a shadow (i.e. a decrease in the intensity of the incident illuminant as the interplay between the incident or direct illuminant and the ambient illuminant in the illumination field) over a single material of a scene depicted in the image. The color change prediction of the BIDR model accurately constrains all color band variations among Type C tokens to illumination field effects occurring in an image by operating as a function of the interplay between the spectral variations occurring between incident illuminant and ambient illuminant components of the illumination field. Thus, BIDR model based constraints couple all color band variations into one integral constraining relationship.

A same texton histogram constraint utilizes Type C tokens and texton histogram operators identified for texture regions within an image. A texton analysis is utilized wherein each pixel of the image (or pixels of those regions of an image identified as comprising a texture) from the recorded color band representation of the respective image file 18, such as, for example, RGB color band values, is converted to a two band representation wherein the two bands comprise a texton label and a texton histogram label. The two band representations are then used to identify texture tokens, as will be described below. A constraint can be imposed that all Type C tokens within the same texture token are of the same mean material.

A log chromaticity similarity constraint utilizes Type C tokens and log chromaticity representation values. The constraining relationship is that those Type C tokens having pixels with similar log chromaticity representation values are constrained to a same color value, with observed differences being attributed to variations in the illumination field.

An X-junction constraint utilizes Type C tokens and X-junction operators. As noted above, an X-junction is an area of an image where a material edge and an illumination boundary cross one another. X-junctions are typically identified by four Type C tokens, two pairs of same material Type C tokens forming the material edge, with each same material pair including an illumination boundary dividing the respective same material into lit and shadowed pairs of Type C tokens. The constraining relationship: 1) a Type B token constraint is imposed between each same material pair of Type C tokens forming the X-junction (those with an illumination boundary between them), and 2) a same illumination constraint is imposed between each pair of Type C tokens forming the material edge of the X-junction. For a more detailed description of X-junctions and the relationships of constituent tokens, reference should be made to U.S. application Ser. No. 11/341,742, filed Jan. 27, 2006, entitled: “Method And System For Identifying Illumination Flux In An Image,” published as US 2006/0177149 on Aug. 10, 2006.

A boundary representation constraint is defined by a standard ratio constraint. An analysis performed on a boundary representation, when indicating a material change, provides an estimate of the ratio of colors between two adjacent regions defined by the boundary, for example, the adjacent Type B tokens, even when the illumination varies over the regions. The constraint states that the ratio of the colors of two adjacent regions is X. The boundary representation analysis is executed at the level of Type B tokens, to classify a boundary as being caused by a material change, then propagated down to the level of the constituent Type C tokens. For a more detailed description of a boundary analysis, at the Type B token level, reference should be made to U.S. application Ser. No. 12/079,878, filed Mar. 28, 2008, entitled “System and Method For Illumination Invariant Image Segmentation.”

According to a feature of the present invention, the boundary representation constraint states that all adjacent pairs of Type C tokens along the boundary, (one Type C token on each side of the boundary, and all of the Type C tokens being within the Type B tokens forming the respective boundary), have colors that satisfy the ratio X, as indicated by the boundary representation analysis.

According to one preferred embodiment of the present invention, each of the above described constraints constrain the spatio-spectral operators to full color values depicted in an image, and can be classified into one of three basic types of constraints, an absolute material color constraint, a same material constraint and a relative reflectance constraint. The absolute material constraint constrains the material at a particular location of an image to be a certain color, as implemented in, for example, the anchor constraint. The same material constraint constrains operators relevant to an image (for example, two pixels or Type C tokens) to be of the same material. The same material type of constraint can be implemented in, for example, Type B, X-junction, log chromaticity similarity, same texton histogram and linear token constraints. The relative reflectance constraint constrains operators relevant to an image (for example, two pixels or Type C tokens) to have a similarity of reflectance characteristics, such as defined by smooth illumination and same illumination constraints, and which can be specified by X-junction, and boundary representation constraints.

According to an exemplary embodiment of the present invention, implemented to be particularly suitable to achieve higher performance and more accurate results in a task such as, for example, face recognition analysis of a digital image, constraints are generated to constrain image properties relative to intensity aspects and chromaticity aspects of the colors depicted in the input image file 18. The constraint based upon the intensity aspects of the image can be expressed as: log t_R+log t_G+log t_B=V, where t_R is the average for the red channel of the material for the pixels of a token t, t_G is the average for the green channel of the material for the pixels of the token t, t_B is the average for the material for the blue channel of the pixels of the token t, and V is the average intensity value for the detail channel, as calculated above. Thus, the sum of the RGB material values of a token, for example, a Type C token, are constrained to the intensity value V.

With reference to constraints based upon chromaticity aspects of the colors depicted in the input image file 18, constraints using the spatio-spectral operators described above are arranged to be constrained relative to chromaticity, for example, a same material chromaticity value. Thus, for two tokens x and y, a same chromaticity constraint can be expressed as: x_R−x_G−y_R+y_G=0 and x_B−x_G−y_B+y_G=0, where x_R is the log value of the red channel of the material color of token x and so on. The constraint expresses the relationship that the difference in log values between red and green, and between blue and green should be the same for the two tokens x and y, being constrained. This correlates to a same chromaticity for the two tokens being considered, such as two Type C tokens in the same Type B token. The constraint solves for the material colors of the respective tokens.

Likewise, a smooth illumination can be arranged to operate only on chromaticity. This is implemented by specifying that the illumination on pixels of two adjacent tokens should only change smoothly. Thus, pixels across the boundary between the two tokens should have the same illumination chromaticity.

An exemplary solver according to the constraint/solver model feature of the present invention comprises a mathematical processing engine for executing an optimizing function, for example, optimization of results in an equation expressed by: [A] [x]=[b], where [A] is a matrix of values that are to be satisfied by (and therefore, taken as solved for by) the definitions of the operator(s) and the constraining relationship(s) for the operator(s), as indicated by selected constraint(s), [x] is a matrix of variables for which the equation is finding an optimal solution, for example, one of an illumination or material component of an image component, for example, a pixel or token, and [b] is a matrix of values observed in an image selected for processing, for example, the recorded values for the RGB color bands of each pixel of an image file 18. The optimizing equation can be implemented in a mathematical optimizing function selected from a set of known optimization solvers such as, for example, known convex optimization operations such as a least squares solver, or a preconditioned conjugate gradient solver.

For example, in an intensity based constraint, the [A] [x]=[b] matrix equation can be expressed as:

[111] [t_R]=[V]

[t_G]

[t_B]

solving for the material values t_R, t_G and t_B, of the token t, relative to the known value V determined via the tone mapping operation, as described above.

According to the embodiment of the present invention suitable for a task such as face recognition, constraints based upon each of intensity and chromaticity aspects of image color provide a result that retains local shading to preserve curvature appearance of such features as nose, chin, etc. while removing strong shadowing that can interfere with an accurate analysis of those features.

According to a further feature of the present invention, factors including bounds, are introduced in a solver operation, in addition to constraining relationships, as a function of real world illumination and material phenomena, to keep material/illumination values within physically plausible ranges, such as a limit1, limit infinity solver (L₁, L_(∞)), a bounded least squares solver, or a bounded L₁, L_(∞) solver, as will be described below.

FIG. 4 shows a functional block diagram of an image segregation system architecture, implemented in, for example, the computer system of FIG. 1, according to a feature of the present invention. Alternatively, the functional blocks of FIG. 4 can be implemented in a dedicated hardware circuit arranged to perform the functionality of the blocks of FIG. 4. An image 32 (as depicted in an image file 18) is input to a preprocessing block 33. The preprocessing block 33 can perform such functions as correction of chromatic aberration in the image 32, combining multiple images to provide a high dynamic range image, linearize pixel data for the image, and so on, for an image optimized for processing. The pre-processed image is then input to a Type C tokenization block 35 which operates to identify Type C tokens in the pre-processed image, in the manner described below. Type C tokens are common to many of the constraints utilized in exemplary embodiments of the present invention, thus, an initial identification of Type C tokens for an input image 32 expedites further processing.

In an exemplary embodiment of the present invention, the CPU 12 executes code to implement both the preprocessing block 33 and the Type C tokenization block 35, as well as a service provider 24, that functions as a central agent and caching structure (configured in the memory 16), to handle an image for processing according to the teachings of the present invention. The service provider 24 receives and stores the pre-processed image and related Type C token information from the Type C tokenization block 35, and is coupled to an operators block 28 (executed by the CPU 12) arranged to generate any other operators for the image required by selected constraints, as will appear. The service provider 24 is also coupled to a global features extraction input 29. The global features extraction input 29 can be used to provide the system with information relevant to an image being processed, such as an indication of light source when the image was taken (sunlight, fluorescent light, incandescent light), time of day, location, domain knowledge, such as information relevant to the nature of the image, such as interior, exterior, buildings, lawns with green grass, trees with leaves in bloom, etc., and any other parameters relevant to image processing. The service provider 24 stores the global features extraction input 29 with a related input image 32.

A constraint builder 26 is coupled to the service provider 24. The constraint builder 26 uses a constraint generator library (configured within the memory 16) that stores the constraint generator software modules for the various constraints described above. The service provider 24 and constraint builder 26 operate to arrange spatio-spectral or color constituent operators relevant to the pre-processed image, according to selected ones of the constraint generator software modules, the resulting constraints being concatenated together, in, for example, the [A] [x]=[b] matrix equation.

A solver 30 (executed by the CPU 12) is coupled to the constraint builder 26, and implements an optimization operation, as described above, for an optimal solution for the [A] [x]=[b] matrix equation, for use in generating intrinsic images from the pre-processed image. The solver 30 is also coupled to a post-processing block 36 (executed by the CPU 12) for certain post-processing operations. The post-processing operations can include, for example, monotonicity maintenance. In monotonicity maintenance, if two large regions exhibit a linear transition in the input image 32, the transition should remain a linear transition in the output intrinsic image 34. Post-processing can also include illumination propagation, that serves to fill in holes left by the solver 30, illumination-map based white balancing and other filtering, smoothing processes. The post-processing block 36 outputs intrinsic images 34.

Referring now to FIG. 5, there is shown a graphical user interface (GUI) for use in connection with an exemplary implementation of the image segregation system architecture feature of the present invention. The GUI of FIG. 5 is displayed on the monitor 20 of the computer system 10 by the service provider 24 for a user to select a desired image segregation operation. The upper left hand corner of the GUI indicates Open Image, Crop Image, Show Parameters, and Segregate selection indicators. A user can move and click a cursor on a desired selector indicator. The Open Image indicator lists all image files 18 currently stored in the memory 16 and enables the user to select an image for processing. The selected image is input 32 (see FIG. 4) to the service provider 24 (via the preprocessing block 33 and the Type C tokenization block 35) which operates to display the selected image at the upper center of the monitor 20 (FIG. 5).

A material image derived by operation of the exemplary segregation system from the selected image is output 34 (see FIG. 4) after execution of the image segregation processing by the solver 30 and displayed at the lower right hand of the monitor 20 (FIG. 5). The derived illumination image is displayed at the lower right hand of the monitor 20 (FIG. 5).

According to a feature of the present invention, the Crop Image selector permits a user to crop a selected image so as to process a portion of the overall image. The Show Parameter selector displays parameters related to the selected image file 18. Parameters for each image file 18 can be stored in a parameter data file associated with a corresponding image file 18, and include any parameters relevant to the processing of the image depicted in the associated image file 18, for example the global features extraction input 29. Parameters can include any data relevant to image processing such as, for example, any variable for pixel analysis by the CPU 12, as for example, in the generation of spatio-spectral operators, and domain knowledge, such as information relevant to the nature of the image, such as interior, exterior, buildings, lawns with green grass, trees with leaves in bloom, etc.

Below the selection indicators is a list of each of the optimizing functions that can be used as the solver 30, and a further list of each of the constraint generators contained in the constraint generator library of the constraint builder 26. A user selects a desired mathematical operation and one or more of the constraints to be imposed upon the selected image. After selection of the image to be processed, the constraints to be imposed and the mathematical operation to be executed, the user can click on the Segregate indicator to commence image segregation processing.

Upon commencement of the image segregation processing, the service provider 24 retrieves the constraint generator software modules for the selected constraints to identify the spatio-spectral operators utilized by the selected constraints. Any spatio-spectral operators not already stored by the service provider 24 are generated by the operators block 28, for the image being segregated, and the service provider 24 caches the results. The cached results can be reused in any subsequent operation for a selected image, with the same set of associated parameters.

For example, if the selected constraint is a same illumination constraint, the service provider 24 identifies Type C tokens, Type B tokens and a token map for the selected image. The Type C tokens were generated by the Type C tokenization block 35. The service provider 24 operates the operators block 28 to generate the remaining operators specified by the same illumination constraint.

Referring now to FIG. 6 a, there is shown a flow chart for generating Type C token regions in the image file of FIG. 2, according to a feature of the present invention. Type C tokens can be readily identified in an image by the Type C tokenization block 35, utilizing the steps of FIG. 6 a. The operators block 28 can then analyze and process the Type C tokens to construct Type B tokens when specified by a selected constraint, as will appear.

Prior to execution of the routine of FIG. 6 a, the CPU 12 can operate to filter the image depicted in a subject image file 18. The filters may include an image texture filter, to, for example, transform patterns of differing reflectance caused by a textured material into a homogeneous representation that captures the spectral and spatial characteristics of the textured region in the image. Identification of Type B tokens can be difficult in an image texture. A textured image contains materials with, for example, more than one reflectance function that manifests as a defining characteristic. For example, the defining characteristic can be a pattern of colors within the texture, such that the texture displays a certain distribution of colors in any patch or region selected from anywhere within the textured region of the image.

A 1^(st) order uniform, homogeneous Type C token comprises a single robust color measurement among contiguous pixels of the image. At the start of the identification routine of FIG. 6 a, the CPU 12 (executing as the Type C tokenization block 35) sets up a region map in memory. In step 100, the CPU 12 clears the region map and assigns a region ID, which is initially set at 1. An iteration for the routine, corresponding to a pixel number, is set at i=0, and a number for an N×N pixel array, for use as a seed to determine the token, is set an initial value, N=N_(start). N_(start) can be any integer>0, for example it can be set at set at 11 or 15 pixels.

At step 102, a seed test is begun. The CPU 12 selects a first pixel, i=(1, 1) for example (see FIG. 2 a), the pixel at the upper left corner of a first N×N sample of the image file 18. The pixel is then tested in decision block 104 to determine if the selected pixel is part of a good seed. The test can comprise a comparison of the color value of the selected pixel to the color values of a preselected number of its neighboring pixels as the seed, for example, the N×N array. The color values comparison can be with respect to multiple color band values (RGB in our example) of the pixel or the filter output intensity histogram representation of the pixel, in the event the image was filtered for texture regions, as described above. If the comparison does not result in approximately equal values (for example, within the noise levels of the recording device for RGB values) for the pixels in the seed, the CPU 12 increments the value of i (step 106), for example, i=(1, 2), for a next N×N seed sample, and then tests to determine if i=i_(max) (decision block 108).

If the pixel value is at i_(max), a value selected as a threshold for deciding to reduce the seed size for improved results, the seed size, N, is reduced (step 110), for example, from N=15 to N=12. In an exemplary embodiment of the present invention, i_(max) can be set at i=(n, m). In this manner, the routine of FIG. 5 a parses the entire image at a first value of N before repeating the routine for a reduced value of N.

After reduction of the seed size, the routine returns to step 102, and continues to test for token seeds. An N_(stop) value (for example, N=2) is also checked in step 110 to determine if the analysis is complete. If the value of N is at N_(stop), the CPU 12 has completed a survey of the image pixel arrays and exits the routine.

If the value of i is less than i_(max), and N is greater than N_(stop), the routine returns to step 102, and continues to test for token seeds.

When a good seed (an N×N array with approximately equal pixel values) is found (block 104), the token is grown from the seed. In step 112, the CPU 12 pushes the pixels from the seed onto a queue. All of the pixels in the queue are marked with the current region ID in the region map. The CPU 12 then inquires as to whether the queue is empty (decision block 114). If the queue is not empty, the routine proceeds to step 116.

In step 116, the CPU 12 pops the front pixel off the queue and proceeds to step 118. In step 118, the CPU 12 marks “good” neighbors around the subject pixel, that is neighbors approximately equal in color value to the subject pixel, with the current region ID. All of the marked good neighbors are placed in the region map and also pushed onto the queue. The CPU 12 then returns to the decision block 114. The routine of steps 114, 116, 118 is repeated until the queue is empty. At that time, all of the pixels forming a token in the current region will have been identified and marked in the region map as a Type C token. In the event the pixels comprise intensity histogram representations, the token can be marked as Type C_(T.)

When the queue is empty, the CPU 12 proceeds to step 120. At step 120, the CPU 12 increments the region ID for use with identification of a next token. The CPU 12 then returns to step 106 to repeat the routine in respect of the new current token region.

Upon arrival at N=N_(stop), step 110 of the flow chart of FIG. 5 a, or completion of a region map that coincides with the image, the routine will have completed the token building task. FIG. 6 b is an original image used as an example in the identification of tokens. The image shows areas of the color blue and the blue in shadow, and of the color teal and the teal in shadow. FIG. 6 c shows token regions corresponding to the region map, for example, as identified through execution of the routine of FIG. 6 a (Type C tokens), in respect to the image of FIG. 6 b. The token regions are color coded to illustrate the token makeup of the image of FIG. 6 b, including penumbra regions between the full color blue and teal areas of the image and the shadow of the colored areas.

Upon completion of the routine of FIG. 6 a by the Type C tokenization block 35, the service provider 24 stores the Type C token region information for the selected image. Prior to commencing any process to generate Type B tokens from the identified Type C tokens, the operators block 28 tests each identified Type C token to make certain that each Type C token encompasses a single material. While each Type C token comprises a region of the image having a single robust color measurement among contiguous pixels of the image, the token may grow across material boundaries.

Typically, different materials connect together in one Type C token via a neck region often located on shadow boundaries or in areas with varying illumination crossing different materials with similar hue but different intensities. A neck pixel can be identified by examining characteristics of adjacent pixels. When a pixel has two contiguous pixels on opposite sides that are not within the corresponding token, and two contiguous pixels on opposite sides that are within the corresponding token, the pixel is defined as a neck pixel.

FIG. 7 shows a flow chart for a neck test for Type C tokens. In step 122, the CPU 12 examines each pixel of an identified token to determine whether any of the pixels under examination forms a neck. The routine of FIG. 6 can be executed as a subroutine directly after a particular token is identified during execution of the routine of FIG. 6 a. All pixels identified as a neck are marked as “ungrowable.” In decision block 124, the CPU 12 determines if any of the pixels were marked.

If no, the CPU 12 exits the routine of FIG. 7 and returns to the routine of FIG. 6 a (step 126).

If yes, the CPU 12 proceeds to step 128 and operates to regrow the token from a seed location selected from among the unmarked pixels of the current token, as per the routine of FIG. 6 a, without changing the counts for seed size and region ID. During the regrowth process, the CPU 12 does not include any pixel previously marked as ungrowable. After the token is regrown, the previously marked pixels are unmarked so that other tokens may grow into them.

Subsequent to the regrowth of the token without the previously marked pixels, the CPU 12 returns to step 122 to test the newly regrown token.

Neck testing identifies Type C tokens that cross material boundaries, and regrows the identified tokens to provide single material Type C tokens suitable for use in creating Type B tokens. FIG. 6 d shows Type B tokens generated from the Type C tokens of FIG. 6 c, according to a feature of the present invention. The present invention provides several exemplary techniques of pixel characteristic analysis for constructing Type B tokens from Type C tokens. One exemplary technique involves arbitrary boundary removal. The arbitrary boundary removal technique can be applied to Type C tokens whether they were generated using N color band values (RGB in our example) of the pixel or the filter output representation of the pixel, in the event the image was filtered. Actual boundaries of any particular Type C token will be a function of the seed location used to generate the token, and are thus, to some extent arbitrary. There are typically many potential seed locations for each particular token, with each potential seed location generating a token with slightly different boundaries and spatial extent because of differences among the color values of the pixels of the various seeds, within the noise ranges of the recording equipment.

FIG. 8 is a flow chart for constructing Type B tokens via an arbitrary boundary removal technique, according to a feature of the present invention. In step 200, the CPU 12 is provided with a set (T_(c)) of Type C tokens generated with a seed size (S) via the routine of FIG. 6 a, with neck removal via the routine of FIG. 7. The seed size S=S_(max), for example, S=4 pixels. In step 202, for each Type C token, t_(c) in the set T_(c) the CPU 12 selects a number (for example 50) of potential seeds s₁ to s_(n). In our example, each selected seed will be a 4×4 pixel array from within the token region, the pixels of the array being of approximately equal values (within the noise levels of the recording device).

In step 204, the CPU 12 grows a new Type C token, utilizing the routines of FIGS. 6 a and 7, from each seed location, s₁ to s_(n) of each token t_(c) in the set T_(c). The newly grown tokens for each token t_(c) are designated as tokens r_(c1) to r_(cn). The newly grown tokens r_(c1) to r_(cn) for each token t_(c) generally overlap the original Type C token t_(c), as well as one another.

In step 206, the CPU 12 operates to merge the newly generated tokens r_(c1) to r_(cn) of each token t_(c), respectively. The result is a new token R_(t) corresponding to each original token t_(c) in the set T_(c). Each new token R_(t) encompasses all of the regions of the respective overlapping tokens r_(c1) to r_(cn) generated from the corresponding original token t_(c). The unions of the regions comprising the respective merged new tokens R_(t) are each a more extensive token than the original Type C tokens of the set. The resulting merged new tokens R_(t) result in regions of the image file 18, each of a much broader range of variation between the pixels of the respective token R_(t) than the original Type C token, yet the range of variation among the constituent pixels will still be relatively smooth. R_(t) is defined as a limited form of Type B token, Type B_(ab1), to indicate a token generated by the first stage (steps 200-206) of the arbitrary boundary removal technique according to a feature of the present invention.

In step 208, the CPU 12 stores each of the Type B_(ab1) tokens generated in steps 202-206 from the set of tokens T_(c), and proceeds to step 210. Type B_(ab1) tokens generated via execution of steps 202-206 may overlap significantly. In step 210, the CPU 12 operates to merge the R_(t) tokens stored in step 208 that overlap each other by a certain percentage of their respective sizes. For example, a 30% overlap is generally sufficient to provide few, if any, false positive merges that combine regions containing different materials. The new set of merged tokens still may have overlapping tokens, for example, previously overlapping tokens that had a less than 30% overlap. After all merges are complete, the CPU 12 proceeds to step 212.

In step 212, the CPU 12 identifies all pixels that are in more than one token (that is in an overlapping portion of two or more tokens). Each identified pixel is assigned to the token occupying the largest region of the image. Thus, all overlapping tokens are modified to eliminate all overlaps.

In step 214, the CPU 12 (as the Type C tokenization block 35 or the operators block 28) stores the final set of merged and modified tokens, now designated as Type B_(ab2) tokens, and then exits the routine. As noted above, the Type B_(ab2) tokens were generated from Type C tokens whether the Type C tokens were generated using N color band values (RGB in our example) of the pixel or the filter output representation of the pixel, in the event the image was filtered.

A second exemplary technique according to the present invention, for using Type C tokens to create Type B tokens, is adjacent planar token merging. The adjacent planar token merging can be implemented when an image depicts areas of uniform color, that is for non-textured regions of an image. Initially, a token graph is used to identify tokens that are near to one another. FIG. 9 shows a flow chart for creating a token graph, containing token map information, according to a feature of the present invention. Each token t_(c) in the set of Type C tokens T_(c), generated through execution of the routines of FIGS. 6 a and 7, is evaluated in terms of a maximum distance D_(max) between tokens defining a neighboring pair of tokens, t_(c), t_(n), of the set T_(c), a minimum number of token perimeter pixels, P_(min), in each token of the neighboring pair of tokens, and a minimum fraction of perimeter pixels, F_(min), of each token of a neighboring pair of tokens, required to be within D_(max).

In step 300, the CPU 12 selects a Type C token t_(c) in the set of Type C tokens T_(c), and identifies the pixels of the selected token t_(c) forming the perimeter of the token. In a decision block 302, the CPU 12 determines whether the number of perimeter pixels is less than P_(min), for example 10 pixels.

If yes, the CPU 12 proceeds to decision block 304 to determine whether there are any remaining tokens t_(c) in the set of Type C tokens T_(c). If yes, the CPU 12 returns to step 300, if no, the CPU 12 exits the routine 306.

If no, the CPU 12 proceeds to step 308. In step 308, the CPU 12 generates a bounding box used as a mask to surround the selected token t_(c). The bounding box is dimensioned to be at least D_(max) larger than the selected token t_(c) in all directions. A known distance transform (for example, as described in P. Felzenszwalb and D. Huttenlocher, Distance Transforms of Sampled Functions, Cornell Computing and Information Science Technical Report TR2004-1963, September 2004), is executed to find the distance from each perimeter pixel of the selected token t_(c) to all the pixels in the surrounding bounding box. The output of the distance transform comprises two maps, each of the same size as the bounding box, a distance map and a closest pixel map. The distance map includes the Euclidean distance from each pixel of the bounding box to the nearest perimeter pixel of the selected token t_(c). The closest pixel map identifies, for each pixel in the distance map, which perimeter pixel is the closest to it.

In step 310, the CPU 12 scans the distance map generated in step 308 to identify tokens corresponding to pixels of the bounding box (from the region map generated via the routine of FIG. 6 a), to identify a token from among all tokens represented by pixels in the bounding box, that has a number N_(cn) of pixels within the distance D_(max), wherein N_(cn) is greater than P_(min), and greater than F_(min)*perimeter pixels of the respective token and the average distance between the respective token and t_(c) is the lowest of the tokens corresponding to the pixels in the bounding box. If these conditions are satisfied, the respective token is designated t_(n) of a possible token pair t_(c), t_(n), and a link L_(cn) is marked active.

In step 312, the CPU 12 checks to determine whether a reciprocal link L_(cn) is also marked active, and when it is marked active, the CPU 12 marks and stores in the token graph, an indication that the token pair t_(c), t_(n) is a neighboring token pair. The reciprocal link refers to the link status in the evaluation of the token designated as t_(n) in the current evaluation. If that token has yet to be evaluated, the pair is not designated as a neighboring token pair until the link L_(cn) is verified as active in the subsequent evaluation of the token t_(n). The CPU 12 then returns to decision block 304 to determine whether there are any further tokens in the set T_(c).

Upon completion of the token graph, the CPU 12 utilizes token pair information stored in the graph in the execution of the routine of FIG. 10. FIG. 10 shows a flow chart for constructing Type B tokens via the adjacent planar token merging technique, according to a feature of the present invention. In the adjacent planer merging technique, pairs of tokens are examined to determine wether there is a smooth and coherent change in color values, in a two dimensional measure, between the tokens of the pair. The color change is examined in terms of a planar representation of each channel of the color, for example the RGB components of the pixels according to the exemplary embodiments of the present invention. A smooth change is defined as the condition when a set of planes (one plane per color component) is a good fit for the pixel values of two neighboring tokens. In summary, neighboring tokens are considered the same material and a Type B token when the color change in a two-dimensional sense is approximately planar.

In step 320, the CPU 12 selects a token pair t_(c), t_(n) from the token graph. In decision block 322, the CPU 12 determines whether the mean color in token t_(c) is significantly different from the mean color in the token t_(c). The difference can be a function of a z-score, a known statistical measurement (see, for example, Abdi, H. (2007), Z-scores, in N. J. Salkind (Ed.), Encyclopedia of Measurement and Statistics, Thousand Oaks, Calif.: Sage), for example, a z-score greater than 3.0.

If the mean colors of the token pair are different, the CPU 12 proceeds to decision block 324 to determine whether there are any additional token pairs in the token graph. If yes, the CPU 12 returns to step 320. If no, the CPU 12 exits the routine (step 326).

If the mean colors are within the z-score parameter, the CPU 12 proceeds to step 328. In step 328, the CPU 12 performs a mathematical operation such as, for example, a least median of squares regression (see, for example, Peter J. Rousseeuw, Least Median of Squares Regression, Journal of the American Statistical Association, Vol. 79, No. 388 (December 1984), pp. 871-880) to fit a plane to each color channel of the pixels (in our example RGB) of the token pair t_(c), t_(n), as a function of row n and column m (see FIG. 2), the planes being defined by the equations:

R=X _(Rn) +Y _(Rm) +Z _(R) G=X _(Gn) +Y _(Gm) +Z _(G) B=X _(Bn) +Y _(Bm) +Z _(B)

wherein parameter values X, Y and C are determined by the least median of squares regression operation of the CPU 12.

Upon completion of the plane fitting operation, the CPU 12 proceeds to step 330. In step 330, the CPU 12 examines each pixel of each of the tokens of the token pair t_(c), t_(n) to calculate the z-score between each pixel of the tokens and the planar fit expressed by the equation of the least median of squares regression operation. When at least a threshold percentage of the pixels of each token of the pair (for example, 80%), are within a maximum z-score (for example, 0.75), then the neighboring token pair is marked in the token graph as indicating the same material in the image. After completion of step 330, the CPU 12 returns to decision block 324.

Upon exiting the routine of FIG. 10, the CPU 12 examines the token graph for all token pairs indicating the same material. The CPU 12 can achieve the examination through performance of a known technique such as, for example, a union find algorithm. (See, for example, Zvi Galil and Giuseppe F. Italiano. Data structures and algorithms for disjoint set union problems, ACM Computing Surveys, Volume 23, Issue 3 (September 1991), pages 319-344). As a simple example, assume a set of seven Type C tokens T₁, T₂, T₃, T₄, T₅, T₆, T₇. Assume that the result of the execution of FIG. 9, (performance of the adjacent planar analysis), indicates that tokens T₁ and T₂ are marked as the same material, and tokens T₁ and T₃ are also marked as the same material. Moreover, the results further indicate that tokens T₄ and T₅ are marked as the same material, and tokens T₅ and T₆ are also marked as the same material. The result of execution of the union find algorithm would therefore indicate that tokens {T₁, T₂, T₃} form a first group within the image consisting of a single material, tokens {T₄, T₅, T₆} form a second group within the image consisting of a single material, and token {T₇} forms a third group within the image consisting of a single material. The groups {T₁, T₂, T₃}, {T₄, T₅, T₆} and {T₇} form three Type B tokens.

A third exemplary technique according to the present invention, for using Type C tokens to create Type B tokens, is a local token analysis. A local token approach generates Type C tokens using a window analysis of a scene depicted in an image file 18. Such tokens are designated as Type C_(w) tokens. FIG. 11 is a flow chart for generating Type C_(w) tokens via the local token analysis technique, according to a feature of the present invention.

In step 400, the CPU 12 places a window of fixed size, for example, a 33×33 pixel array mask, over a preselected series of scan positions over the image. The window can be a shape other than a square. The scan positions are offset from one another by a fixed amount, for example ½ window size, and are arranged, in total, to fully cover the image. The window area of pixels at each scan position generates a Type C_(w) token, though not every pixel within the window at the respective scan position is in the Type C_(w) token generated at the respective scan position.

At each scan position (step 402), the CPU 12 operates, as a function of the pixels within the window, to fit each of a set of planes, one corresponding to the intensity of each color channel (for example, RGB), and an RGB line in RGB space, characterized by a start point I₀ and an end point I₁ of the colors within the window. The planar fit provides a spatial representation of the pixel intensity within the window, and the line fit provides a spectral representation of the pixels within the window.

For the planar fit, the planes are defined by the equations:

R=X _(Rn) +Y _(Rm) +Z _(R) G=X _(Gn) +Y _(Gm) +Z _(G) B=X _(Bn) +Y _(Bm) +Z _(B)

wherein parameter values X, Y and C are determined by CPU 12 by executing a mathematical operation such as the least median of squares regression discussed above, a least-squares estimator, such as singular value decomposition, or a robust estimator such as RANSAC (see, for example, M. A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM, Vol 24, pp 381-395, 1981).

For the RGB line fit, the line is defined by: I(r,g,b)=I₀(r,g,b)+t(I₁(r,g,b)−I₀(r,g,b)) wherein the parameter t has a value between 0 and 1, and can be determined by the CPU 12 utilizing any of the mathematical techniques used to find the planar fit.

At each scan position, after completion of step 402, the CPU 12 operates in step 404 to examine each pixel in the window in respect of each of the planar fit representation and RGB line representation corresponding to the respective window scan position. For each pixel, the CPU 12 determines an error factor for the pixel relative to each of the established planes and RGB line. The error factor is related to the absolute distance of the pixel to its projection on either from either the planar fit or the RGB line fit. The error factor can be a function of the noise present in the recording equipment or be a percentage of the maximum RGB value within the window, for example 1%. Any pixel distance within the error factor relative to either the spatial planar fit or the spectral line fit is labeled an inlier for the Type C_(w) token being generated at the respective scan position. The CPU 12 also records for the Type C_(w) token being generated at the respective scan position, a list of all inlier pixels.

At each scan position, after completion of step 404, the CPU 12 operates in step 406 to assign a membership value to each inlier pixel in the window. The membership value can be based upon the distance of the inlier pixel from either the planar fit or the RGB line fit. In one exemplary embodiment of the present invention, the membership value is the inverse of the distance used to determine inlier status for the pixel. In a second exemplary embodiment, a zero-centered Gaussian distribution with a standard deviation is executed to calculate membership values for the inlier pixels.

After all of the scan positions are processed to generate the Type C_(w) tokens, one per scan position, the CPU 12 operates to compile and store a token data list (step 408). The token data list contains two lists. A first list lists all of the pixels in the image file 18, and for each pixel, an indication of each Type C_(w) token to which it labeled as an inlier pixel, and the corresponding membership value. A second list lists all of the generated Type C_(w) tokens, and for each token an indication of the inlier pixels of the respective token, and the corresponding membership value. After compiling and storing the token data list, the CPU 12 exits the routine (step 410).

FIG. 12 is a flow chart for constructing Type B tokens from the Type C_(w) tokens generated via the local token analysis technique, according to a feature of the present invention. In step 420, the CPU 12 calculates a similarity of parameters of the spatial planer dimensions and spectral RGB lines of adjacent or overlapping Type C_(w) tokens generated through execution of the routine of FIG. 108. Overlapping and adjacent Type C_(w) tokens can be defined as tokens corresponding to scan positions that overlap or are contiguous. A similarity threshold can be set as a percentage of difference between each of the spatial planer dimensions and spectral RGB lines of two overlapping or adjacent Type C_(w) tokens being compared. The percentage can be a function of the noise of, for example, the camera 14 used to record the scene of the image file 18. All overlapping or adjacent Type C_(w) token pairs having a calculated similarity within the similarity threshold are placed on a list.

In step 422, the CPU 12 sorts the list of overlapping or adjacent Type C_(w) token pairs having a calculated similarity within the similarity threshold, in the order of most similar to least similar pairs. In step 424, the CPU 12 merges similar token pairs, in the order of the sort, and labeling pairs as per degree of similarity. Each merged token pair will be considered a Type_(B) token. In step 426, the CPU 12 stores the list of Type_(B) tokens, and exits the routine.

In a further exemplary embodiment of the present invention, the CPU 12 (executing as the operators block 28) compiles lists of Type B tokens separately generated through each of and/or a combination of one or more of the arbitrary boundary removal, adjacent planar token merging, and local token analysis techniques. The determination of the combination of techniques used depends in part on whether a particular region of the image was filtered because of texturing of the image. Since each Type B token generated through the described techniques likely represents a single material under varying illumination conditions, merging sufficiently overlapping Type B tokens generated through the use of varying and different techniques, provides a resulting, merged Type B token that represents a more extensive area of the image comprising a single material, and approaches the extent of a Type A token.

Sufficiently overlapping can be defined by satisfaction of certain pixel characteristic criteria, such as, for example:

-   A) The two Type B tokens have at least n of the original Type C     tokens in common, for example, n=1 -   B) The two Type B tokens have at least n pixels in common, for     example, n=20 -   C) The two Type B tokens have at least n % overlap, that is at least     n % of the pixels in a first one of the two Type B tokens are also     found in the second one of the two Type B tokens or vice versa,     wherein, for example n %=10%. -   D) The percentage of pixels in a smaller one of the two Type B     tokens, also found in the larger one of the two Type B tokens is     above a preselected threshold, for example 15%. -   E) A preselected combination of criteria A-D.

Merging of two sufficiently overlapping Type B tokens can be accomplished via a mathematical operation such as execution of the union find algorithm discussed above. In the case of two overlapping Type B tokens that do not satisfy the above discussed criteria, the overlapping pixels of the two tokens can be assigned to the larger one of the two Type B tokens.

As a result of execution by the Type C tokenization block 35 and/or the operators block 28 (via the CPU 12) of the token generation and merging techniques according to features of the present invention, an image can be accurately segmented into tokens representing discrete materials depicted in the scene (Type B tokens) and tokens representing regions of robust similar color (Type C tokens), thus providing a basis for computational efficiencies, as the token representations capture spatio-spectral information of a significant number of constituent pixels. The service provider 24 stores all of the Type C and Type B tokens generated through execution of the above described token generation techniques, along with the relevant token map information, for example, as determined during execution of the adjacent planar token merging technique, and cross-references the stored operator results to the associated selected image file 18, for use in any segregation processing of the selected image.

In our example of a same illumination constraint, the service provider 24 identifies Type C and Type B tokens as the operators required by the selected constraint. The Type C tokenization block 35 generated the Type C tokens. The service provider 24 operates the operators block 28 to execute the above described techniques, to generate the relevant Type B tokens for the image 32, as well as a token map. The constraint builder 26 organizes the generated token operators according to the exemplary matrix equation, [A] [x]=[b], for input to the solver 30. In the same illumination constraint, the constraining relationship of the relevant constraint generator software module is that adjacent Type C tokens, as indicated by the token map information, are lit by the same illumination, unless the adjacent Type C tokens are part of the same Type B token.

Each Type C token stored by the service provider 24 is identified by a region ID, and includes a listing of each constituent pixel by row and column number. Each pixel of a Type C token will be of approximately the same color value, for example, in terms of RGB values, as all the other constituent pixels of the same Type C token, within the noise level of the equipment used to record the image. An average of the color values for the constituent pixels of each particular Type C token can be used to represent the color value for the respective Type C token. Each Type B token is identified by constituent Type C tokens, and thus can be processed to identify all of its constituent pixels via the respective constituent Type C tokens.

Pursuant to a feature of the present invention, a model for image formation reflects the basic concept of an image as comprising two components, material and illumination. This relationship can be expressed as: I=ML, where I is the image color, as recorded and stored in the respective image file 18, M the material component of the recorded image color and L the illumination component of the recorded image color. The I value for each Type C token is therefore the average color value for the recorded color values of the constituent pixels of the token.

Thus: log(I)=log(ML)=log(M)+log(L). This can be restated as i=m+l, where i represents log(I), m represents log(M) and l represents log(L). In the constraining relationship of the same illumination constraint, in an example where three Type C tokens, a, b and c, (see FIG. 13) are adjacent (and not within the same Type B token, (as can be shown by a comparison of row and column numbers for all constituent pixels)), l_(a)=l_(b)=l_(c). Since: l_(a)=i_(a)−m_(a), l_(b)=i_(b)−m_(b), and l_(c)=l_(c)−m_(c,) these mathematical relationships can be expressed as (1)m_(a)+(−1)m_(b)+(0)m_(c)=(i_(a)−i_(b)), (1)m_(a)+(0)m_(b)+(−1)m_(c)=(i_(a)−i_(c)) and (0)m_(a)+(1)m_(b)+(−1)m_(c)=(i_(b)−i_(c)).

FIG. 13 shows a representation of an [A] [x]=[b] matrix equation for the mathematical relationships of the example of the three adjacent Type C tokens a, b and c described above, as constrained by the same illumination constraint: the adjacent Type C tokens a, b and c are at the same illumination. In the matrix equation of FIG. 15, the various values for the log (I), in the [b] matrix, are known from the average recorded pixel color values for the constituent pixels of the adjacent Type C tokens a, b and c, generated by the Type C tokenization block 35 from the image selected for segregation. The [A] matrix of 0's, 1's and −1's, is defined by the set of equations expressing the selected same illumination constraint, as described above. The number of rows in the [A] matrix, from top to bottom, corresponds to the number of actual constraints imposed on the tokens, in this case three, the same illumination between three adjacent Type C tokens. The number of columns in the [A] matrix, from left to right, corresponds to the number of unknowns to be solved for, again, in this case, three. Therefore, the values for the material components of each Type C token a, b and c, in the [x] matrix, can be solved for in the matrix equation. It should be noted that each value is actually a vector of three values corresponding to the RGB color bands of our example.

Accordingly, the matrix equation of FIG. 13, as arranged by the constraint builder 26, is input by the constraint builder 26 to the solver 30 for an optimized solution for the values of the material components of the adjacent Type C tokens a, b and c of the selected image. As noted above, in the exemplary GUI embodiment of the present invention, a user selects one of several mathematical techniques for finding the optimal solution to the system of constraint equations, [A] [x]=[b]. The CPU 12 configures the solver 30 according to the mathematical operation selected by the user.

For example, in a standard least squares solver, the matrix equation is restated as \underset{x}{min}(Ax−b)². The solver 30 then executes the least squares operation to determine optimized values for each of m_(a), m_(b) and m_(c). The solver 30 can then proceed to generate and display a material image based upon the optimal m_(a), m_(b) and m_(c) values. In the material image, the m_(a), m_(b) and m_(c) values are substituted for the originally recorded RGB values, for each pixel of the respective tokens. The solver 30 can proceed to also generate an illumination image from the known recorded image values i_(a), i_(b), i_(c), and the determined m_(a), m_(b) and m_(c) values, utilizing the model expressed by i=m+l.

Each of the material and illumination images are displayed on the monitor 20, via, for example, the GUI (see FIG. 5) and can be stored by the service provider 24, and cross-referenced to the original image file 18. The intrinsic images are then used in a face recognition step, for example, step 1010 in FIG. 3, as described above.

According to a further feature of the present invention, the solver 30 can be configured to introduce factors including bounds that capture the limits of real world illumination and material phenomena, to keep material/illumination values determined by the optimization procedures as solutions, [x], to within physically plausible ranges. This can be implemented, for example, in an iterative technique to introduce additional inequality constraints on out-of-bounds values in [x], at each iteration, and executed to resolve towards values within the defined bounds. Thus, the above described least squares technique can be augmented to include minimum and maximum bounds on individual material estimates (as expressed by the entries of [x]). Moreover, the entries of [x] can be regularized such that the material estimates are consistent with a priori knowledge of material properties.

In an exemplary embodiment of the present invention, the matrices used in the least squares solver to specify the selected constraints, [A] and [b] are subject to the following bounds, expressed by the problem:

a linear least squares formulation:min_(x′):Σ_(i)(A_(i) ^(T)x′−t_(i))²

-   subject to: x′≧α_(m)1     -   x′≦ω_(m)1     -   x′≧img_(j)         where 1 denotes the vector of all ones, α_(m), the darkest         possible material value (for example, a material cannot be         darker than coal), and ω_(m), the brightest possible material         value. The img_(j) value is the log intensity value at a         particular token j, to provide a constraint based upon the real         world observation that a segregated material color cannot be         darker than it appeared in the original image, since         illumination can only brighten the apparent color of an observed         material.

In the linear least squares formulation, the unique minimum solution for x′ is the material map that minimizes, in a linear system expressed by A^(T) Ax′=A^(T) t, the average squared difference between the target material differences t_(i) and the estimated differences A_(i) ^(T)x′. For example, if the “ith” constraint A_(i) dictates that two tokens a & b are the same material, A^(T) Ax′ takes the difference between the values of tokens a & b in x′ and computes the distortion from the target value t_(i)=0.

The inequalities expressed by the “subject to” bounds set forth above, form a feasible set of material solutions x′ which satisfy the real world constraints of possible maximum and minimum material color values. This differs from the standard, known least squares solution in that x′, if not further constraint by the “subject to” bounds, could take on a value at a given location of an image (for example, at a particular pixel or token) that violates the real world observations of reflectance, yet achieves a more optimal solution for the min x′ formulation.

In the optimization process executed by the solver 30, whenever any tokens have material color values that violate the “subject to” inequalities, at a particular iteration of the process, additional temporary constraints are added that pin the material values in violation, to values that satisfy the bounding conditions. Thus, the original matrices [A] and [b] are augmented with new matrices specifying the new bounding constraints A_(bounds) and b_(bounds) (as an expression of the “subject to” bounds) to define a new augmented system of matrix equations [A; A_(bounds)] [x]=[b, b_(bounds)]. The augmented system of equations can be solved analogously to the original system, for example, using the known least squares procedure.

In accordance with the above described bounded feature of the present invention, additional, temporary constraints are added whenever color values violate real world phenomena. A re-solving of the augmented equations can be repeated, as necessary, starting with the original system A^(T) Ax′=A^(T) t, each time (i.e. the temporary bounding constraints need not be carried over between iterations), and iteratively solving augmented systems A′^(T) A′x′=A′^(T) t′ until the “subject to” bounds are satisfied.

In accordance with yet another feature of the present invention, an L₁, L_(∞) objective function provides a regularization of the optimized solution by encoding a preference for a small number of material changes. In effect, the L₁, L_(∞) solver includes the a priori belief that material maps should contain a small number of materials in a figure-of-merit. In the solver of the system, there is a distinction between the objective function, a formula that assigns a figure-of-merit to every possible solution, and the algorithm used to find a solution, an optimal value according to a given objective function. As the problem in our exemplary embodiment is stated as a minimization, min_(x′):Σ_(i)(A_(i) ^(T)x′−t_(i))², the value an objective function assigns can be characterized as a “cost.”

In our problem, let x′ be a matrix of a number of rows of tokens and a number of columns of color bands, where x′^(c) denotes the c^(th) column associated with the c^(th) color band. The least squares objective function, in formula, is augmented, as follows:

min_(x′):Σ_(c)Σ_(i)(A_(i) ^(T)x′^(c)−t^(c)i)²+γ Σ_(k|tk) max_(c)|A_(k) ^(T)x′^(c)|

where γ|γ>0 governs the trade-off between the cost associated with the least squares term and the L₁, L_(∞) penalty. The expression Σ_(k|tk) max_(c)|A_(k) ^(T)x′^(c)| accumulates the maximum per-channel absolute difference over all the same material constraints in [A].

For example, given a same material constraint between tokens a & b, the L₁, L_(∞) function will only include a term for a color channel with the largest difference in between x^(c) _(a) and x^(c) _(b) over color channel c. In an exemplary embodiment of the present invention, the optimization procedure, for example as expressed by the objective function min_(x′):Σ_(c)Σ_(i)(A_(i) ^(T)x′^(c)−t^(c)i)²+γ Σ_(k|tk) max_(c)|A_(k) ^(T)x′^(c)|, is a shrinkage technique. That is, a sequence of least squares problems is solved in a manner wherein, at each round, constraint targets determined to violate the same material constraint are shrunk. At the end of the sequence, constraints with a value below a given threshold are culled from the constraint system, and a new least squares solution is computed. It should be noted that bounds such as the “subject to” bounds discussed above, can be added to the objective function to provide a bounded L₁, L_(∞) solver.

FIG. 14 is a generalized functional block diagram for the service provider 24 and constraint builder 26. To summarize the above described constraint examples in a general scheme, a selection is made of an image 32, and a number of constraint generators from a set of constraint generators 1, 2, . . . N, (the constraint generator software modules) for example, by a user, via the GUI. The set of constraint generators 1-N includes the constraints described above, and any additional constraining relationships developed as a function of spatio-spectral information for an image. The above described set of constraints is provided as an example. In a face recognition operation, the constraints would include, for example, a same material chromaticity constraint, and the log t_R+log t_G+log t_B=V intensity constraint, as described above. The present invention contemplates any constraining relationship based upon spatio-spectral operators, constituents of color, such as intensity and chromaticity, or any other basis that provides a logical deduction regarding material and illumination aspects of an image, and thus a basis for constructing matrices [A] and [b] to define a set of equations whose optimal solution captures intrinsic illumination and material components of a given image.

Likewise, a set of operators 1-M, generated by the Type C tokenization block 35 or the operators block 28, includes all operators defined in the constraint generator modules 1-N, including the V value. As shown in FIG. 14, the service provider 24 provides all of the operators 1-M, as required by the selected constraint generators 1-N and further couples the selected constraint generators 1-N to a constraint assembly 39 via a logical switch 40 (both configured within the constraint builder 26). In the event any of the operators 1-M for a selected image 32 are not already stored by the service provider 24, the service provider 24 utilizes the operators block 28 to compute such operators on demand, including a tone mapping operation, and generation of V, in the manner described above. The constraint assembly 39 constructs a separate [A] [x]=[b] matrix for each one of the selected constraint generators, as a function of the operators and the constraining relationships defined in the respective constraint generators 1-N. In each case, the [A] [x]=[b] matrix is constructed in a similar manner as described above for the same illumination example.

Upon completion of the construction of the system of equations [A]_(i) [x]=[b]_(i), for each of the selected constraint generators, i={1, 2, . . . N}, the constraint assembly 39 concatenates the constituent matrices [A]_(i), [b]_(i), from each constraint generator. Since each of the concatenated equations may contain a different subset of the unknowns, [x], the assembly is performed such that corresponding columns of individual matrices [A]_(i), that constrain particular unknowns in [x], are aligned. The concatenated matrices, [A] [x]=[b], are then input to the solver 30, for solution of the unknowns in the complete [x] vector, pursuant to the selected optimization procedure, for output of intrinsic images 34. The individual constraints within the concatenated matrices, [A] [x]=[b], can be weighted relative to one another as a function of factors such as perceived importance of the respective constraint, strength or empirically determined confidence level.

The above described example of a same illumination constraint utilizes Type C token and Type B token spatio-spectral operators. These token operators provide an excellent representation of images that include large surface areas of a single material, such as are often depicted in images including man-made objects, or other objects such as a human face. However, in many natural scenes there are often large areas of highly textured regions, such as sand, grass, stones, foliage, and so on. As noted above, identification of Type B tokens using Type C tokens, can be difficult in an image texture. According to a further feature of the present invention, a texton histogram operator provides a mechanism for capturing statistically uniform spatial variations of textured regions in a manner that is useful in a constraint based optimization, for example, as expressed by the [A] [x]=[b] matrix equation.

Thus, according to this feature of the present invention, rather than generating Type C tokens in textured regions of an image, from intensity histograms, for use in identifying Type B tokens, as described above, texture tokens are generated as a species of Type B tokens, for use in a constraint. In an exemplary embodiment of the texton histogram operator, the operators block 28 converts each pixel of the image (or pixels of those regions of an image identified as comprising a texture) from the recorded color band representation of the respective image file 18, such as, for example, RGB color band values, to a two band representation wherein the two bands comprise a texton label and a texton histogram label. The two band representations for the pixels are then used to identify texture tokens, as will appear.

A texton label for each pixel is generated through execution of a clustering process. A texture can be characterized by a texture primitive (for example, in a grass texture, a single blade of grass), and the spatial distribution of the primitive. A texton analysis is an analytical method for characterizing a texture primitive, for example via a clustering algorithm. Clustering is a process for locating centers of natural groups or clusters in data. In an exemplary embodiment of the present invention, the data comprises pixel patches selected from among the pixels of an image being segregated into material and illumination components. For example, 3×3 pixel patches are clustered into K different groups, with each group being assigned a designating number (1,2,3, . . . K). The texton label for each pixel of the 3×3 array is the group number of the group to which the respective patch was assigned during the clustering process.

To expedite execution of a clustering algorithm, random samples of 3×3 patches can be selected throughout the image, or region of the image identified as comprising a texture, for processing in a clustering algorithm. After execution of the clustering algorithm by the CPU 12 (operating as the operators block 28), each 3×3 patch of the image is assigned the texton label of the closest one of the K group centers identified in the clustering process, as executed in respect of the selected random samples.

To advantage, prior to execution of a clustering algorithm, the pixels of the image are subject to an image intensity normalization. In a clustering process utilizing an intensity-based distance matrix, dark areas of an image may be placed in a single group, resulting in an under representation of groups for shadowed areas of a textured region of an image. A normalization of the image provides a more accurate texton representation for texture regions under varying illumination. A normalized intensity for a pixel can be expressed by:

i _(norm)(n,m)=log(i(n,m)/i _(b)(n,m)),

where i_(norm)(n,m) is the normalized intensity for a pixel p(n,m), i(n,m) is the intensity for the pixel p(n,m), as recorded in the image file 18, and i_(b)(n,m) is a blurred or low passed filtered version of the pixel p(n,m). For example, a 10 pixel blur radius can be used in any standard blurring function.

Clustering can be executed according to any known clustering algorithm, such as, for example, K means clustering where there are K clusters or groups S_(i), i=1,2, . . . K, and μ_(i) is the mean point or center point of all the data points x_(j) ε S_(i). In our example, each x_(j) comprises a selected 3×3 pixel patch arranged as a 9×1 vector of the nine pixels in the patch (27 elements total, including the RGB values of each of the nine pixels of the vector). As noted above, each mean point μ_(i) is assigned a texton label, 1, 2, 3 . . . K, that becomes the texton label for any pixel of a 3×3 patch clustered into the group for which the respective mean point is the center.

According to an exemplary embodiment of the present invention, the CPU 12 executes the algorithm by initially partitioning the selected 9×1 vectors, representing 3×3 pixel patches of the image, into K initial groups S_(i). The CPU 12 then calculates a center point μ_(i), for each group S_(i,) utilizing an intensity-based distance matrix. After determining a center point μ_(i), for each group S_(i), the CPU 12 associates each 9×1 vector to the closest center point μ_(i), changing groups if necessary. Then the CPU 12 recalculates the center points μ_(i). The CPU 12 executes iterations of the steps of associating each 9×1 vector to the closest center point μ_(i), and recalculating the center points μ_(i), until convergence. Convergence is when there is no need to change the group for any of the 9×1 vectors. At that point, the CPU 12 assigns the group number for the respective center point μ_(i), as the texton label for the pixels of each vector in that group.

As noted above, pixels of 3×3 patches not selected as samples for clustering are assigned the texton label of the closest one of the K group centers μ_(i), identified in the clustering process, as executed in respect of the selected random samples. A texton label map is stored by the service provider 24, and is coextensive with the pixel array of FIG. 2. In the texton label map, for each pixel location, there is an indication of the respective texton label.

Upon completion of the texton label assignment for pixels of the image, the CPU 12 operates to generate a texton histogram for each pixel to provide a representation of the spatial variation of texton representations within a textured region of the image. To that end, the CPU 12 accesses the texton label map. At each pixel location within the texton label map, a pixel patch of, for example, 21×21 pixels, is set up around the current location. The 21×21 patch size is far greater than the 3×3 patch sized used to generate the texton representations, so as to capture the spatial variations of the texture. A texton histogram is then generated for the pixel location at the center of the 21×21 patch, in a similar manner as the intensity histogram described above. However, rather than bins based upon color band values, in the texton histogram, there is a bin for each texton label value, 1,2, 3 . . . K. The count for each bin corresponds to the number of pixels in the 21×21 patch having the texton label value for the respective bin.

When a texton histogram is generated for each pixel of the texton label map, the CPU 12 executes a second clustering step. In the second clustering step, the texton histograms are clustered using spectral clustering. Spectral clustering techniques use a spectrum of a similarity matrix of data of interest, (in our example, the texton histograms) to reduce the dimensionality for clustering in fewer dimensions. A similarity matrix for a given set of data points A can be defined as a matrix S where S_(ij) represents a measure of the similarity between points i, j ε A. In our example, eigenvectors of the Laplacian are clustered using a mean shift. The distance metric is a chi-squared distance of the histograms.

A texton histogram label (1,2 . . . ) is assigned to each cluster group defined by the clustering procedure. For each pixel of the texton label map, the texton histogram label for the cluster group corresponding to a texton histogram that is nearest the texton histogram for the respective pixel, is assigned to that pixel. Distance is defined as the chi-squared histogram distance. Upon completion of the assignment of a texton histogram label to each pixel, each pixel is now represented by a two band, texton label, texton histogram label representation.

According to a feature of the present invention, the two band, texton label, texton histogram label representations for pixels of an image file 18 can be utilized in a constraint for construction of an [A]_(i) [x]=[b]_(i) constituent within the concatenated matrices, [A] [x]=[b]. For example, it can be assumed that a region of an image wherein contiguous pixels within the region all have the same two band, texton label, texton histogram label representation, comprises a region of the same mean material of a texture depicted in the image. Such a region can be referred to as a texture token, a species of a Type B token. Thus, a constraint can be imposed that all Type C tokens within the same texture token are of the same mean material. In this constraint, the Type C tokens are the Type C tokens generated from the color band values of the constituent pixels by the Type C tokenization block 35.

While the above exemplary embodiment of the present invention has been described with a user selecting constraint generators and mathematical operations via a GUI, the image segregation processing can be done in other operating modes, such as automatically, with images, constraint generators and mathematical operations being automatically selected, for example, as a function of image parameters.

In the preceding specification, the invention has been described with reference to specific exemplary embodiments and examples thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative manner rather than a restrictive sense. 

1. An automated, computerized method for processing an image, comprising the steps of: providing an image file depicting an image, in a computer memory; identifying information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color; defining a constraint as a function of the information; and utilizing the constraint in an image segregation operation.
 2. The method of claim 1 wherein the information relevant to spatio-spectral aspects of comprises spatio-spectral operators.
 3. The method of claim 1 wherein the constituents of color include intensity and chromaticity.
 4. The method of claim 1 wherein the constraint is arranged to preserve shading contributing to an appearance of curvature of features depicted in the image, and to reduce shadowing effects.
 5. The method of claim 4 wherein the constraint comprises a constraint based upon a tone mapping of the image.
 6. The method of claim 5 wherein the constraint based upon a tone mapping of the image is expressed by log t_R+log t_G+log t_B=V, where V is an average for a calculated intensity value for a token t, derived from the tone mapping.
 7. The method of claim 6 wherein the constraint includes a second constraint based upon a chromaticity aspect of color depicted in the image.
 8. An automated, computerized method for processing an image, comprising the steps of: providing an image file depicting an image, in a computer memory; organizing information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image; and utilizing the matrix equation in an image segregation operation.
 9. The method of claim 8 wherein the information relevant to spatio-spectral aspects of an image comprises spatio-spectral operators.
 10. The method of claim 8 wherein the constraining relationship is arranged to preserve shading contributing to an appearance of curvature of features depicted in the image, and to reduce shadowing effects.
 11. A computer system which comprises: a CPU; and a memory storing an image file defining an image; the CPU being arranged and configured to execute a routine to identify information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, define a constraint as a function of the information and utilize the constraint in an image segregation operation.
 12. A computer system which comprises: a CPU; and a memory storing an image file defining an image; the CPU being arranged and configured to execute a routine to organize information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image and utilize the matrix equation in an image segregation operation.
 14. A computer program product, disposed on a computer readable media, the product including computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory; identify information in the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color; define a constraint as a function of the information; and utilize the constraint in an image segregation operation.
 15. The computer program product of claim 14 wherein the information relevant to spatio-spectral aspects of an image comprises spatio-spectral operators.
 16. The computer program product of claim 14 wherein the constituents of color include intensity and chromaticity.
 17. The computer program product of claim 14 wherein the constraint is arranged to preserve shading contributing to an appearance of curvature of features depicted in the image, and to reduce shadowing effects.
 18. The computer program product of claim 17 wherein the constraint comprises a constraint based upon a tone mapping of the image.
 19. The computer program product of claim 18 wherein the constraint based upon a tone mapping of the image is expressed by log t_R+log t_G+log t_B=V, where V is an average for a calculated intensity value for a token t, derived from the tone mapping.
 20. The computer program product of claim 19 wherein the constraint includes a second constraint based upon a chromaticity aspect of color depicted in the image.
 21. A computer program product, disposed on a computer readable media, the product including computer executable process steps operable to control a computer to: provide an image file depicting an image, in a computer memory; organize information for the image file relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, in a matrix equation expressed by: [A] [x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the information relevant to a logical deduction regarding material and illumination aspects of an image and selected from information relevant to spatio-spectral aspects of an image and constituents of color, [b] expresses information obtained form the image file, in the computer memory, and [x] expresses an unknown material/illumination component of the image; and utilize the matrix equation in an image segregation operation.
 22. The computer program product of claim 21 wherein the information relevant to spatio-spectral aspects of an image comprises spatio-spectral operators.
 23. The compute program product of claim 21 wherein the constraining relationship is arranged to preserve shading contributing to an appearance of curvature of features depicted in the image, and to reduce shadowing effects.
 24. The computer program product of claim 23 wherein the constraining relationship comprises a constraint based upon a tone mapping of the image.
 25. The computer program product of claim 24 wherein the constraint based upon a tone mapping of the image is expressed by log t_R+log t_G+log t_B=V, where V is an average for a calculated intensity value for a token t, derived from the tone mapping.
 26. The computer program product of claim 25 wherein the constraint includes a second constraint based upon a chromaticity aspect of color depicted in the image. 